Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach
Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach
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探测器系统中 MHz ADC 整形脉冲的定时和表征：比较研究和深度学习方法
DOI：
10.1088/1748-0221/14/03/p03002
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发表时间：
2019-01
影响因子：
1.3
通讯作者：
Zhang F
中科院分区：
文献类型：
--
作者：
Ai P;Wang D;Huang G;Fang N;Xu D;Zhang F
基于模数转换器的定时系统广泛应用于高能物理探测器的设计中。在本文中，我们提出了一种基于深度学习的新方法，从有限的ADC样本集中提取时间信息。首先，定量分析了传统的曲线拟合方法对三种变化（长期漂移、短期变化和随机噪声）的影响，并给出了仿真实例。接下来，对曲线拟合和神经网络进行了比较研究，以证明深度学习在这个问题上的潜力。仿真结果表明，在非理想条件下，该专用网络结构能有效抑制噪声均方根，提高定时分辨率。最后，用ALICE PHOS FEE卡进行了实验。在实验条件下，该方法的性能比曲线拟合方法提高了20%以上。
Timing systems based on Analog-to-Digital Converters are widely used in the design of previous high energy physics detectors. In this paper, we propose a new method based on deep learning to extract the time information from a finite set of ADC samples. Firstly, a quantitative analysis of the traditional curve fitting method regarding three kinds of variations (long-term drift, short-term change and random noise) is presented with simulation illustrations. Next, a comparative study between curve fitting and the neural networks is made to demonstrate the potential of deep learning in this problem. Simulations show that the dedicated network architecture can greatly suppress the noise RMS and improve timing resolution in non-ideal conditions. Finally, experiments are performed with the ALICE PHOS FEE card. The performance of our method is more than 20% better than curve fitting in the experimental condition.
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DOI：
10.1016/j.nima.2018.03.052
发表时间：
2018-04
期刊：
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
影响因子：
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作者：
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通讯作者：
K. Mahata;A. Shrivastava;J. Gore;S. Pandit;V. Parkar;K. Ramachandran;A. Kumar;S. Gupta;P. Patale
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发表时间：
2003-05
影响因子：
1.3
作者：
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通讯作者：
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发表时间：
2021-07
期刊：
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影响因子：
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A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
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10.1109/icmla.2016.0160
发表时间：
2016-01
期刊：
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
影响因子：
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Evan Racah;Seyoon Ko;Peter Sadowski;W. Bhimji;C. Tull;Sang-Yun Oh;P. Baldi;Prabhat
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10.5555/1756006.1953039
发表时间：
2010-03
期刊：
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影响因子：
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作者：
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通讯作者：
Pascal Vincent;H. Larochelle;Isabelle Lajoie;Yoshua Bengio;Pierre-Antoine Manzagol